20 research outputs found
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point Processes
Graph convolutional networks (GCNs) have achieved great success in graph
representation learning by extracting high-level features from nodes and their
topology. Since GCNs generally follow a message-passing mechanism, each node
aggregates information from its first-order neighbour to update its
representation. As a result, the representations of nodes with edges between
them should be positively correlated and thus can be considered positive
samples. However, there are more non-neighbour nodes in the whole graph, which
provide diverse and useful information for the representation update. Two
non-adjacent nodes usually have different representations, which can be seen as
negative samples. Besides the node representations, the structural information
of the graph is also crucial for learning. In this paper, we used
quality-diversity decomposition in determinant point processes (DPP) to obtain
diverse negative samples. When defining a distribution on diverse subsets of
all non-neighbouring nodes, we incorporate both graph structure information and
node representations. Since the DPP sampling process requires matrix eigenvalue
decomposition, we propose a new shortest-path-base method to improve
computational efficiency. Finally, we incorporate the obtained negative samples
into the graph convolution operation. The ideas are evaluated empirically in
experiments on node classification tasks. These experiments show that the newly
proposed methods not only improve the overall performance of standard
representation learning but also significantly alleviate over-smoothing
problems.Comment: Accepted by IEEE TNNLS on 30-Aug-2023. arXiv admin note: text overlap
with arXiv:2210.0072